Evidence map›Paper›PMID 38023704›Full record

ArticleBioengineering & translational medicine2023

Machine learning chained neural network analysis of oxygen transport amplifies the physiological relevance of vascularized microphysiological systems.

James J Tronolone, Tanmay Mathur, Christopher P Chaftari, Yuxiang Sun, Abhishek Jain

Open access · goldAbstract read
In one paragraph

Article in Bioengineering & translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Integration of microphysiological systems with computational and digital twin modeling for pharmaceutical development: a systematic review.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2026
    Pooled it
  2. Organ-on-a-chip toxicology.Innovation (Cambridge (Mass.)) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

James J TronoloneDepartment of Biomedical Engineering, College of Engineering Texas A&M University College Station Texas USA.
Tanmay MathurDepartment of Biomedical Engineering, College of Engineering Texas A&M University College Station Texas USA.ORCID https://orcid.org/0000-0003-2123-9857
Christopher P ChaftariDepartment of Biomedical Engineering, College of Engineering Texas A&M University College Station Texas USA.
Yuxiang SunDepartment of Nutrition, College of Agriculture and Life Sciences Texas A&M University College Station Texas USA.
Abhishek JainDepartment of Biomedical Engineering, College of Engineering Texas A&M University College Station Texas USA.ORCID https://orcid.org/0000-0003-2235-5139
Texas A&M University · USTexas A&M Health Science Center · USTexas College · US

Funding

Determinants of COVID19-induced venous thrombosis and targeted therapy assessed with bioengineered vein-chipR01HL157790 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI CONNOR, JOHN H, COOKE, JOHN P · 2021 to 2024
$2.8M
NHLBI NIH HHS R01 HL157790
6 · The paper itself

Abstract

Since every biological system requires capillaries to support its oxygenation, design of engineered preclinical models of such systems, for example, vascularized microphysiological systems (vMPS) have gained attention enhancing the physiological relevance of human biology and therapies. But the physiology and function of formed vessels in the vMPS is currently assessed by non-standardized, user-dependent, and simple morphological metrics that poorly relate to the fundamental function of oxygenation of organs. Here, a chained neural network is engineered and trained using morphological metrics derived from a diverse set of vMPS representing random combinations of factors that influence the vascular network architecture of a tissue. This machine-learned algorithm outputs a singular measure, termed as vascular network quality index (VNQI). Cross-correlation of morphological metrics and VNQI against measured oxygen levels within vMPS revealed that VNQI correlated the most with oxygen measurements. VNQI is sensitive to the determinants of vascular networks and it consistently correlates better to the measured oxygen than morphological metrics alone. Finally, the VNQI is positively associated with the functional outcomes of cell transplantation therapies, shown in the vascularized islet-chip challenged with hypoxia. Therefore, adoption of this tool will amplify the predictions and enable standardization of organ-chips, transplant models, and other cell biosystems.

Indexed as

artificial intelligencecell transplantisletmachine learningmicrophysiological systemvascularization

Identifiers

PMID38023704
PMCPMC10658488
OpenAlexW4385483723

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.